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阅读中眼动标准分析中的假阳性及其他统计误差。

False Positives and Other Statistical Errors in Standard Analyses of Eye Movements in Reading.

作者信息

von der Malsburg Titus, Angele Bernhard

机构信息

Department of Psychology and Department of Linguistics, UC San Diego.

Department of Psychology, Bournemouth University.

出版信息

J Mem Lang. 2017 Jun;94:119-133. doi: 10.1016/j.jml.2016.10.003. Epub 2016 Dec 9.

Abstract

In research on eye movements in reading, it is common to analyze a number of canonical dependent measures to study how the effects of a manipulation unfold over time. Although this gives rise to the well-known multiple comparisons problem, i.e. an inflated probability that the null hypothesis is incorrectly rejected (Type I error), it is accepted standard practice not to apply any correction procedures. Instead, there appears to be a widespread belief that corrections are not necessary because the increase in false positives is too small to matter. To our knowledge, no formal argument has ever been presented to justify this assumption. Here, we report a computational investigation of this issue using Monte Carlo simulations. Our results show that, contrary to conventional wisdom, false positives are increased to unacceptable levels when no corrections are applied. Our simulations also show that counter-measures like the Bonferroni correction keep false positives in check while reducing statistical power only moderately. Hence, there is little reason why such corrections should not be made a standard requirement. Further, we discuss three statistical illusions that can arise when statistical power is low, and we show how power can be improved to prevent these illusions. In sum, our work renders a detailed picture of the various types of statistical errors than can occur in studies of reading behavior and we provide concrete guidance about how these errors can be avoided.

摘要

在阅读眼动研究中,通常会分析一些典型的因变量指标,以研究某种操作的效果如何随时间展开。尽管这会引发众所周知的多重比较问题,即原假设被错误拒绝(I型错误)的概率虚增,但不应用任何校正程序却是公认的标准做法。相反,似乎有一种普遍的观点认为校正没有必要,因为误报率的增加太小无关紧要。据我们所知,从未有人提出过正式论据来证明这一假设。在此,我们报告一项使用蒙特卡洛模拟对该问题进行的计算研究。我们的结果表明,与传统观点相反,不进行校正时误报率会增加到不可接受的水平。我们的模拟还表明,像邦费罗尼校正这样的对策能够控制误报率,同时仅适度降低统计效力。因此,几乎没有理由不将此类校正作为标准要求。此外,我们讨论了统计效力较低时可能出现的三种统计错觉,并展示了如何提高效力以防止这些错觉。总之,我们的工作描绘了阅读行为研究中可能出现的各种统计误差的详细情况,并就如何避免这些误差提供了具体指导。

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